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This is the third of three articles that quantify the high added value of frequent satellite microwave observations of the atmosphere (with a “refresh” time on the order of 1 min) to capture the dynamics of weather systems. Recent advances in small-satellite and microwave miniaturization, such as the “Temporal Experiment for Storms and Tropical systems” (TempEST) millimeter-wave radiometer developed at the Jet Propulsion Laboratory, are paving the way for the design of convoys of spaceborne radars that can directly observe the evolution of severe weather at very fine temporal scales. The analyses presented here are to establish the relation between passive microwave observations and their change in time and the underlying cloud variables and processes and to evaluate the sensitivity to the different physical and instrument parameters. In this third part, simulations are used to demonstrate and quantify the direct sensitivity of a time sequence of mm-wave radiances to the vertical structure of the vertical updrafts in convective storms. It is demonstrated that the brightness temperatures from a pair of low-Earth-orbit (LEO) radiometers maintaining a separation of 1 or 2 min can indeed be used to retrieve the column-maximum magnitude of the vertical wind as well as the height of the maximum. While such passive retrievals do not provide the vertical detail that a pair of radars would produce, the radiometers have a vast swath and therefore enable the observation of an entire storm. The application is therefore quite different from the case of a convoy of radars: rather than compiling radar statistics over multiple years, the radiometer-convoy measurements can be used to analyze every storm that is observed. [ABSTRACT FROM AUTHOR]

An effective filtering technique is required for rainfall rate measurement by weather radar. A Jensen–Shannon distance (JSD)-based thresholding filter is proposed to mitigate nonmeteorological signals, either in clear air or rain situations. This algorithm classifies range-Doppler bins into two classes, hydrometeors and nonhydrometeors, based on spectral polarimetric variable features. The result is a mask to be applied on the spectrograms. The variable selected here is the spectral co-polar correlation coefficient, available in dual-polarization and full polarimetric radars. The algorithm first does global thresholding by finding an optimized threshold value based on the averaged clear-air spectral polarimetric variable distribution. Next, classical filtering steps are carried out like a ground clutter notch filter around 0 ms−1, a mathematical morphology to fill gaps in the hydrometeor areas, and a removal of narrow Doppler power spectra. The second part of this article is the assessment of filtering techniques without ground truth. An assessment without ground truth is useful to select optimal algorithm configurations from a large solution space. Criteria of good filtering are defined both in the spectral and time domain. Based on those criteria, subjective and objective unsupervised evaluation metrics are derived, with a focus on the objective ones. Data, including clear air and rain collected from a full polarimetric Doppler X-band radar in the urban area, are used. With the proposed unsupervised evaluation metrics, the JSD-based thresholding filter is compared to two spectral polarimetric filters. Overall, the JSD-based filter performs very well considering both the subjective and the objective evaluation metrics. [ABSTRACT FROM AUTHOR]

Radar sounders (RSs) are gaining importance in planetary missions thanks to their unique capability of providing direct measurements of subsurface (SS) structures. To support their design and data interpretation, several electromagnetic (e.m.) simulation techniques have been developed with enhanced capabilities for emulating the RS acquisition process. However, the raw simulated radargrams obtained from e.m. simulators are difficult to interpret and analyze without a focusing operation, which results in an underestimation of the RS detection performance. While frequency methods for range and azimuth compression of real RS data are well-established, their use on simulated data is not addressed in the literature and requires major modifications. This article presents a novel method that implements azimuth compression using unfocused and focused processing on simulated raw data. The proposed method is based on an adaptation of the range-Doppler algorithm to the case of raw data generated by a coherent RS simulator. The method is demonstrated in three case studies to show the similarity between simulated and real data processing: 1) simple geometries; 2) a simulated SHAllow RADar (SHARAD) radargram compared with the real data product; and 3) a real application scenario for supporting the design of a new RS instrument. The results indicate higher fidelity of the focused simulated data with the real data product and the target structure, confirming the usefulness of the proposed approach in obtaining realistic processing of simulated radargrams. [ABSTRACT FROM AUTHOR]

A small-aperture receiving array occupying a small area provides convenience for the layout of high-frequency (HF) radars. However, this kind of antennas has a wide beam, which adversely affects the wave measurements. To solve this problem, a method for extracting wave parameters from wide-beam radar echoes, which models current variations across the beam, is proposed in this article. The nondirectional wave spectrum is extracted from the echoes of a small-aperture HF radar, and wave parameters such as the significant wave height, peak, and mean wave period are then calculated from it. Simulation is carried out to assess the performance of the method for different current velocities, radar frequencies, and wind conditions. The proposed method is then applied to a nine-day dataset collected by a multifrequency HF radar with a circular receiving array (MHF-C) radar for further validation via comparisons between the radar-estimated and the buoy-measured wave parameters. Some factors that may affect the performance of wave height measurements are analyzed, such as the signal-to-noise ratio and radial current velocity. The correlation coefficient (CC) between the radar-estimated and the buoy-measured significant wave height is 0.90, and the root mean square difference (RMSD) is 0.51 m. For the mean wave period, the CC is 0.61 and the RMSD is 0.67 s. The results demonstrate that the significant wave height and the mean wave period extracted by the proposed method are consistent with the buoy-measured values, and the proposed method can overcome the broadening influence of the Doppler spectrum caused by ocean currents. [ABSTRACT FROM AUTHOR]

In this article, we propose a novel processing algorithm for frequency-modulated continuous-wave (FMCW) ice sounding radar, which addresses the along-track focusing of internal reflecting horizons. It is a modified frequency scaling algorithm that can handle the two-layer medium imaging model as well as the Doppler impact of the motion within the sweep in a nadir-looking radar. The proposed algorithm’s comprehensive derivation and implementation processes are described. In the range-Doppler domain, a really effective formulation for radar signals is also presented, which considers refraction effects and electromagnetic wave propagation velocity changes at the interface of two different media. Point target simulations are carried out to demonstrate the algorithm’s performance. The proposed method is also tested on the real data acquired by the shallow-layer-detection ice sounding radar (SLDISR). The result shows the applicability of the proposed method for imaging the ice sheet. [ABSTRACT FROM AUTHOR]

Spaceborne radar sounders are high frequency (HF)/very high frequency (VHF) nadir-looking sensors devoted to subsurface investigations. Their data interpretation can be severely hindered by off-nadir surface clutter. Recent literature showed that the clutter suppression capabilities of this class of systems can be greatly enhanced by deploying an array of orbiting sensors in formation flight synthesizing a narrow radar antenna beam. In this article, we assess the capability of distributed radar sounding to discriminate clutter from subsurface returns by exploiting direction of arrival (DOA) estimation techniques. This is achieved by first outlining an approach for designing and evaluating the distributed radar sounder DOA estimation performance as function of the radar system parameters (e.g., intersensor distance) and external noise factors such as ionospheric scintillations. Then, the theory is complemented by radar simulations of several acquisitions over Greenland assuming a variety of subsurface geometries. The simulations confirm that clutter discrimination through DOA estimation is a viable approach to further improve the array capability in disambiguation of subsurface echoes from surface ones. [ABSTRACT FROM AUTHOR]

Weather radar echo extrapolation, which predicts future echoes based on historical observations, is one of the complicated spatial–temporal sequence prediction tasks and plays a prominent role in severe convection and precipitation nowcasting. However, existing extrapolation methods mainly focus on a defective echo-motion extrapolation paradigm based on finite observational dynamics, neglecting that the actual echo sequence has a more complicated evolution process that contains both nonlinear motions and the lifecycle from initiation to decay, resulting in poor prediction precision and limited application ability. To complement this paradigm, we propose to incorporate a novel long-term evolution regularity memory (LERM) module into the network, which can memorize long-term echo-evolution regularities during training and be recalled for guiding extrapolation. Moreover, to resolve the blurry prediction problem and improve forecast accuracy, we also adopt a coarse–fine hierarchical extrapolation strategy and compositive loss function. We separate the extrapolation task into coarse and fine two levels which can reduce the downsampling loss and retain echo fine details. Except for the average reconstruction loss, we additionally employ adversarial loss and perceptual similarity loss to further improve the visual quality. Experimental results from two real radar echo datasets demonstrate the effectiveness of our methodology and show that it can accurately extrapolate the echo evolution while ensuring the echo details are realistic enough, even for the long term. Our method can further be improved in the future by integrating multimodal radar variables or introducing certain domain prior knowledge of physical mechanisms. It can also be applied to other spatial–temporal sequence prediction tasks, such as the prediction of satellite cloud images and wind field figures. [ABSTRACT FROM AUTHOR]

Accurate translational motion compensation is critical for inverse synthetic aperture radar (ISAR) imaging, especially for datasets with low signal-to-noise ratio (SNR) or sparse aperture. Among the existing translational motion compensation methods, the methods that based on the polynomial model have profound noise robustness but are easily affected by the variation of the reference distance. The methods that based on the range profile alignment have good versatility but are far from ideal under low SNR and sparse aperture environment. Therefore, we propose a novel and robust translational motion compensation method to overcome the shortcoming of the existing methods. Specifically, we estimate the translational phase error sequence by maximizing Laplacian average gray level and peak value. It can improve the robustness of translational motion compensation for noise and sparse aperture. Since such principle creates a large-scale multiobjective optimization problem, we design weighted optimization framework—second-order drift particle swarm optimization (WOF—SDPSO) to solve it accurately. WOF-SDPSO achieves full exploration of high-dimensional variable space by variable dimension reducing, random drift update, and second-order oscillation convergence. These ensure the global optimal solution easier to be found. Experimental results of the measured dataset prove that the proposed method is effective and accurate under the conditions of low SNR and sparse aperture. Moreover, the proposed method is noise robust for polynomial translational motion model with sparse aperture. [ABSTRACT FROM AUTHOR]

In this study, we analyzed low-frequency drone-borne ground-penetrating radar (GPR) and full-wave inversion for soil electrical conductivity mapping. Indeed, in the lowest GPR frequency ranges, the soil surface reflexion coefficient depends more on the soil electrical conductivity than on its permittivity. Numerical experiments were conducted within the frequency range 15–45 MHz to analyze parameter sensitivities, the well-posedness of the inverse problem as well as the depth of sensitivity. The results show that the soil surface reflexion is significantly more sensitive to the soil electrical conductivity than the soil permittivity. Therefore, the conductivity can be retrieved using full-wave inversion within this frequency range, with a characterization depth varying from 0.5 to 1 m, depending on the soil properties. Yet, the permittivity also affects the results and should be accounted for in the inversion strategy. Field measurements were performed using low-frequency drone-borne radar with a 5-m half-wave dipole antenna, and electromagnetic induction (EMI) measurements with different depth sensitivities were conducted for comparison. Kriging interpolation was used to get maps from measurement points. The soil conductivity maps obtained by the proposed GPR and EMI are compliant in terms of absolute values and spatial patterns. This study demonstrated the capacity of low-frequency drone-borne GPR for fast, field-scale soil electrical conductivity mapping. [ABSTRACT FROM AUTHOR]

For a space-based radar system with hybrid baseline, the problem of clutter angle-Doppler spectral broadening poses a significant challenge to clutter cancellation in the terrain fluctuant observation scene. To improve the robustness of clutter suppression, this article proposed a homogeneous sample selection method based on a novel concept of generalized spatial spectrum density function (GSSDF). This method can be summarized as three crucial steps. First, the GSSDF was constructed by the prior information of digital elevation model (DEM) data, radar system parameters, and the backscattering model. Then, the angle of deflection (AOD) and the equivalent bandwidth (EBW) of GSSDF were adopted to measure the diffusion of clutter spectral. Subsequently, the sample selection criterion was established by a new threshold detection strategy, but the key here is that an appropriate detection threshold of the AOD and EBW can be determined by the characteristic of filter response. To summarize, this approach ensures that the training samples sharing similar clutter properties can be selected to estimate clutter covariance matrix (CCM), thereby enhances clutter suppression capability. Finally, the experimental results demonstrated that the proposed method can obtain better clutter suppression performance than the other contrast methods. [ABSTRACT FROM AUTHOR]

The spatial correlation of ice-sounding data can be used to trace internal isochronic layers and synchronize the age–depth relationship between different ice core sites, which is difficult using existing imaging methods. In this study, we propose a new algorithm to address the opportunity that applying spatial correlation to ice-sheet imaging offers. The algorithm is a spatial-correlation-based ice-sounding imaging method using fast back projection (FBP) that successfully improves the spatial correlation of imaging results with high efficiency. We give the specific steps to implement the algorithm and apply it to simulate both point targets and ice-sounding radar data to demonstrate its validity in imaging ice sheets. Furthermore, compared with two previous methods, the proposed algorithm improves the spatial correlation without degrading the ability of signal-to-noise ratio (SNR) improvement and processing efficiency. [ABSTRACT FROM AUTHOR]

Radar echo extrapolation is a common approach to weather nowcasting, which has become a significant support to detect potential disastrous weather a few hours ahead. The dynamics pattern inside an echo intensity sequence is beneficial for echo prediction. However, existing extrapolation methods have limited ability to consider entire time-series echo context in an entire echo sequence from a given historical timestamp to a given future timestamp, thus leading to low long-term extrapolation accuracy. To solve this issue, we introduce spatiotemporal self-attention and propose a deep learning model named the nonlocal echo dynamics (NLED) network to capture the dependencies of the entire time domain. The NLED network has an encoder–decoder architecture for extrapolation. The encoder decomposes historical echoes into features of multiple spatial scales, which makes it better at learning echo dynamics from the global scale to the local scale. The decoder employs nonlocal blocks with sparse self-attention related to echo dynamics to learn correlations in the entire echo event, which is beneficial for predicting long-term echo distributions. Our model is evaluated on radar reflectivity datasets from Shanghai and Hong Kong. The experimental results indicate that the NLED model achieves a more accurate long-term forecast and alleviates the forgetting of stronger echo dynamics, thus validating the effectiveness of entire time-series modeling by the NLED network. [ABSTRACT FROM AUTHOR]

In the complex and changeable marine environment, it is difficult to achieve high-performance detection based on statistical theory for small sea surface detectors in practical applications. In this work, a method for detecting small targets on the sea surface based on sequence features is proposed from the perspectives of feature extraction and feature classification on a basis of real data. The sequence-feature method first extracts three sequence features from the radar echo sequence, namely, the instantaneous phase (IP), Doppler spectrum entropy, and short-time Fourier transform (STFT) marginal spectrum. The features are used to train the bidirectional long short-term memory (Bi-LSTM) networks model so that it has the ability to distinguish the target and the sea clutter. The measured dataset verifies that the proposed detector can achieve the detection of sea surface targets with controllable false alarm rate (FAR), and the performance is better than several existing detectors. [ABSTRACT FROM AUTHOR]

Recent investigations using polarimetric decomposition and numerical models have helped to improve the understanding of how radar signals interact with lake ice. However, further research is needed on how radar signals are impacted by varying lake ice properties. Radiative transfer (RT) models provide one method of improving this understanding. These are the first published experiments using the snow microwave RT (SMRT) model to investigate the response of different frequencies (L-, C-, and X-band) at horizontal-horizontal (HH) and vertical-vertical (VV) polarizations using various incidence angles (20°, 30°, and 40°) to changes in ice thickness, porosity, bubble radius, and ice–water interface roughness. This is also the first use of SMRT in combination with a thermodynamic lake ice model. Experiments were for a lake with tubular bubbles and one without tubular bubbles under difference scenarios. An analysis of the backscatter response to different properties indicates that increasing ice thickness and layer porosity have little impact on backscatter from lake ice. X-band backscatter shows increased response to surface ice layer bubble radius; however, this was limited to other frequencies except at shallower incidence angles (40°). All three frequencies display the largest response to increasing root mean square (rms) height at the ice–water interface, which supports surface scattering at the ice–water interface as being the dominant scattering mechanism. These results demonstrate that the SMRT is a valuable tool for understanding the response of backscatter to changes in freshwater lake ice properties and could be used in the development of inversion models. [ABSTRACT FROM AUTHOR]

Dynamic deformation measurement is an important approach to monitor structure stability. Due to its rapid imaging capabilities, ground-based multi-input–multi-output (GB-MIMO) radar has shown great application potential in bridge structure monitoring. This article proposes a dynamic deformation estimation method based on radar image series. First, an improved clutter suppression method is used to overcome the estimation error in complex working status. Second, a two-step pixel extraction method is adopted to ensure both the quantity and quality of sample pixels. Finally, frequency- and time-domain environmental stimulating methods are jointly considered for stable mode shape estimation. This article systematically measured the dynamic deformations of bridge structures, including two suspension bridges and a cable-stayed bridge. The time-series deformation, deflection, vibration frequency, and amplitude on the bridge structure are obtained by image-domain measurement. For the first time, the mode shapes of bridges are obtained through GB-MIMO radar. The correctness of structure parameter measurement is verified with the finite element model (FEM). Experimental results prove that with the proposed method, stable results could be acquired against weak and complex stimulation conditions. [ABSTRACT FROM AUTHOR]

This article presents detailed geometric analyses of ground clutter in bistatic passive airborne radars. Analytic closed-form solutions are derived for finding the intersection of iso-ranges and iso-velocities. This allows clutter bistatic coordinates to be easily converted to its Cartesian coordinates. Based on those solutions, a theoretical clutter map in the bistatic coordinates can be calculated assuming uniform clutter distribution on the Earth’s surface. This is done by first converting a resolution cell in the bistatic coordinates into a corresponding cell in the Cartesian coordinates. Next, the power distribution in the bistatic coordinates is calculated taking into account range dependency, incident angles, and the radiation patterns of the transmitter and the receiver. The aim of this analysis is to characterize clutter in terms of expected mean power map rather than in terms of statistical distribution. The obtained results provide insights into the expected characteristics of clutter, which can be helpful in designing signal processing algorithms for ground moving target indication (GMTI) and synthetic aperture radar (SAR). The theoretical clutter distribution on the range-velocity map is compared with real-life data acquired with a Digital Video Broadcasting–Terrestrial (DVB-T)-based passive radar and good agreement between theory and measurement is presented. [ABSTRACT FROM AUTHOR]

The holographic subsurface radar (HSR) has been a promising geophysical electromagnetic technique for detecting shallowly buried targets with high lateral resolution image. However, radar images are considerably interpreted by strong reflections from rough surface and inhomogeneity in media of interest. In this article, we focus on mitigating the clutter in HSR applications using a learning-based approach, which requires neither prior information regarding the penetrable medium characteristics nor analytic framework to describe the through-medium interference. The generative adversarial network (GAN) with attentive subspace projection is developed to remove the clutter and recover the target image. The subspaces containing target response are selected with the multihead attention preliminarily. Then, the generative network will further focus on the target regions, and the discriminative network will assess the generated results locally and globally. Experiments using real data were conducted to demonstrate the effectiveness of our approach. The visual and quantitative results show that the proposed approach achieves superior performance on removing clutter in HSR images compared with the state-of-the-art clutter mitigation approaches. [ABSTRACT FROM AUTHOR]

To realize high azimuth resolution for sea-surface targets, this article proposes a superresolution imaging method that relies on the Weibull distribution. The proposed method introduces the generalized Gaussian distribution and Weibull distribution to represent the statistical distribution function of the target prior information and sea clutter, respectively. The corresponding objective function was derived under the maximum a posteriori (MAP) criterion. To address the nonlinearity of the objective function, this article adopts the Newton–Raphson iterative method to resolve it. Simulations and experimental data assessment indicate that the proposed method has superior superresolution imaging performance compared with other traditional superresolution methods for sea-surface target imaging. [ABSTRACT FROM AUTHOR]

Typhoons, a kind of devastating natural disaster, have caused incalculable damages worldwide. The meteorological radar image is essential for weather forecasting, especially typhoons. The weather nowcasting (future 0–6 h) can be implemented via extrapolating radar images without using the primary weather forecasting method—the numerical weather prediction model. However, the existing related techniques based on statistics or artificial intelligence were not efficient enough. In this article, a novel radar image extrapolation algorithm named dynamic multiscale fusion-generative adversarial network (DMSF-GAN) was proposed. DMSF-GAN captures the future radar image distribution based on current radar images through modifying the GAN. In the generative module of GAN, an auto-encoder consisting of dynamic inception-3-D and feature connection blocks extracts significant features from current radar images. The feasibility of the proposed model was verified on a real radar image dataset, and the experimental results proved that the proposed algorithm could effectively capture the location and pattern of the future radar echo, especially for typhoon weather systems. Compared with the mainstream methods of radar image extrapolation such as optical-flow and recurrent neural network (RNN)-based models, DMSF-GAN has a more superior and robust performance, which is also suitable for running on low-configuration machines. [ABSTRACT FROM AUTHOR]

Microwave photonic synthetic aperture radar (MWP-SAR) offers a larger signal bandwidth than conventional SAR, and its theoretical resolution can be improved to centimeter level. To achieve same order of magnitude resolution in the azimuth direction, long synthetic aperture is always required, which results in extremely high requirements for the accuracy of imaging procedure. Compared with the conventional SAR imaging algorithms, two major problems should be considered: 1) the scattering characteristics of target might vary from the frequency of signal and the angle of incidence, which seriously affects the coherence of received echo and 2) the imaging of the MWP-SAR system is more sensitive to motion errors and therefore requires higher accuracy of motion compensation processing. To solve the above issues, a high-precision imaging method for MWP-SAR is proposed. First, based on the attribute scattering center (ASC) model, this article analyzes the target scattering characteristics with different frequencies and incident angles. Then, according to the influence of scattering phase on MWP-SAR imaging, an optimal subaperture division algorithm is proposed to guarantee the coherence of each subaperture data and better imaging results. Furthermore, to compensate for the effects of high-order motion errors, this article proposes a motion error estimation algorithm based on subimage registration, where the full-aperture high-order motion errors can be divided into multiple linear components, and the flight trajectory of platform can be accurately reconstructed. Finally, a full-aperture time-domain high-precision imaging method is presented, and the effectiveness of the proposed formation is verified by both simulation and actual airborne MWP-SAR data processing. [ABSTRACT FROM AUTHOR]

This article presents a new method to retrieve dynamic information from spaceborne Doppler radar observations. The method is based on a complex convolution Doppler resampling (CConDoR) formulation, which links the spaceborne pulse-pair (PP) correlation measurements to high-resolution PP products that are not affected by the spacecraft motion. The CConDoR formalism allows to easily simulate Doppler products via convolution products. The CConDoR representation enables also retrievals by deconvolution. This approach is illustrated using a Wiener deconvolution algorithm. Results are shown for simulations of the radar of the Earth Cloud Aerosol Radiation Explorer (EarthCARE; developed by the European and Japanese space agencies). The proposed corrections improve the accuracy of the measured mean velocities (by correcting for nonuniform beam-filling (NUBF) biases) and spectral widths (by correcting for the spectral broadening), which to the best of the authors’ knowledge is novel. [ABSTRACT FROM AUTHOR]

Sky-wave over-the-horizon radar (OTHR) propagates radio waves off the ionosphere to provide long-range surveillance around the Earth’s curvature. Frequency selection for high-latitude and polar OTHRs is challenging unless there is an environmental monitor that addresses the significant ionospheric variability in high-latitude regions, a spectrum monitor that finds unoccupied frequencies in the high-frequency (HF) radio waveband, and a frequency management system (FMS) that selects an optimal frequency from the merged results of the environmental monitor and spectrum monitor. This article describes the first FMS for high-latitude OTHR that merges results from the environmental monitor and spectrum monitor in real-time. The environmental monitor uses the assimilative Canadian high-arctic ionospheric model (A-CHAIM), which assimilates near-real-time data and is, to date, the most advanced ionospheric model for high-latitude regions. Despite remaining limitations, demonstrations of this real-time FMS between a transmitter and four targets during the day and night and during fall and summer show that it could be one of the tools for selecting frequencies for operational scenarios. [ABSTRACT FROM AUTHOR]

Lake ice thickness (LIT), a thematic product of lakes as an essential climate variable (ECV), is sensitive to changes in air temperature and on-ice snow mass. Here, a novel and efficient analytic method (retracking approach) is presented for the estimation of LIT from Ku-band (13.6 GHz) radar altimetry data. The new retracker, referred to as ${{\mathtt {LRM}}\_{}{\mathtt {LIT}}}$ , is based on the physical modeling of the conventional radar echoes (also called low-resolution mode or LRM) over ice-covered lakes that show a characteristic step-like feature in their leading edge attributed to the reflection of radar waves at the snow–ice and ice–water interfaces. The method is applied to Jason-2 and Jason-3 data acquired over Great Slave Lake, Canada, over three ice seasons (2013–2016). As expected, the agreement between the Jason-2 and Jason-3 LIT estimates over their overlapping period (2016 ice season) is excellent with a mean bias error of 0.013 m and root mean square error (RMSE) of 0.024 m. LIT estimates from ${{\mathtt {LRM}}\_{}{\mathtt {LIT}}}$ are in good agreement with simulations from a thermodynamic lake ice model and in situ measurements with RMSE values of the order of a few centimeters for the three winter seasons. The retracker also provides a robust way to assess the accuracy of LIT estimates which is in the order of 0.10 m when the ice cover is well established and prior to melt onset. In addition, ${{\mathtt {LRM}}\_{}{\mathtt {LIT}}}$ captures the seasonal transitions during the freeze-up and breakup periods and ice growth over different winter seasons, making it a promising method for monitoring inter-annual variability and trends in LIT from past and current conventional radar altimetry missions. [ABSTRACT FROM AUTHOR]

Target detection in sea clutter is of great significance in military radar research. Because the characteristics of sea clutter are complex and easily affected by wind direction, the method of suppressing sea clutter by using sea clutter characteristics to reproduce is not advantageous. To address this problem, this article focuses on the direct extraction of the target echo. According to the difference in fractional characteristics between the target echo and sea clutter, fractional Fourier transform (FRFT) is introduced to extract the target echo. In this article, a fractional domain change coefficient clustering method is established to identify the range units of the target, and a fractional domain filtering method based on the minimax is proposed to extract the target spectrum of target-dominated range units, so as to separate the target echo from sea clutter. Simulated and measured experiments prove that this method can accurately identify targets and extract target information under various sea states and a different number of targets. [ABSTRACT FROM AUTHOR]

Ambiguity clutter for airborne radar systems is usually caused by a uniform pulse repetition interval (UPRI) waveform, which significantly degrades target detection and location performance. To address this issue, this article proposes an ambiguity clutter suppression method resorting to a pseudorandom pulse repetition interval (PrPRI) waveform. First, an airborne radar clutter model accounting for multiple range rings with PrPRI waveform is developed. Next, a nonuniform coherent processing framework is introduced to eliminate the clutter folded in the range and Doppler domain. In particular, the reasons for the enlargement of the area free of clutter corresponding to PrPRI mode are analyzed, as well as the maximum unambiguous Doppler frequency, maximum unambiguous range, and design principles for PRI are derived. Finally, numerical examples are designed to verify that the nonuniform coherent processing framework can enlarge the clutter-free area and achieve unambiguity target detection and location. [ABSTRACT FROM AUTHOR]

In this article, a spatial-temporally varying generalized Pareto intensity distribution (STV-GPID) model is presented to characterize large-scene sea clutter in high-resolution maritime surveillance radars, and a multiscan recursive Bayesian bipercentile (MSRB-BiP) estimation method is proposed to implement the outlier-robust estimation of parameters in the STV-GPID model. Considering that sea clutter characteristics are affected by sea states and the viewing geometry of a radar, the large scene is segmented into clutter map cells based on an empirical backscattering coefficient model of sea surface to predict radar cross section (RCS) of sea surface per unit physical area. Sea clutter intensities on each clutter map cell are modeled by a GPID. In the parameter estimation, the data of previous scans are transformed into the prior information on the parameters to reduce the storage burden of radar systems. The MSRB-BiP estimator updates the parameters of the STV-GPID model recursively by a mixed sample set with the returns of the present scan and simulated data using the prior information. The mixture ratio adjusts the forgetting rate of data to adapt to temporally varying characteristics of sea clutter. At least, it brings three merits: low storage requirement, outlier robustness, and mitigation of spatial small sample size of a single scan. The convergence and robustness of the estimation method are verified by simulated data. The experimental results on two measured radar datasets verify the effectiveness of the MSRB-BiP estimators, and the errors at the steady state are reduced at least 17.7% and 66.7%, respectively. [ABSTRACT FROM AUTHOR]

A novel GNU radio-based software-defined radio receiver using universal serial radio peripheral (USRP) front end and Yagi antenna is developed to record meteor echoes in passive mode using FM broadcast. Pilot observations are made for four days in August–September 2020 to record FM signals at 103.2, 104, and 107.5 MHz with a varying sample rate of 8 and 200 kHz. The in-phase and quadrature signals are processed to obtain the amplitude and phase of the received signals for identification of the meteor echoes. A new algorithm is developed to automatically detect the echoes using a normalized amplitude threshold and a minimum temporal separation between two echoes. Overdense meteor echoes with a plateau-like amplitude response are found using a ratio test and remaining echoes are rejected. Thus, the system can detect almost all types of meteor echoes reported earlier. Notably, signatures of fragmentation and background wind are also observed in the form of different phase patterns. Histogram of the meteor counts shows a peak for shorter durations indicating larger populations of the smaller size of meteoroids. The log–log plot of the meteor duration versus count is found to exhibit a characteristic transition of the slope. An empirical estimate of the height of the echoes is obtained. The echoes show micrometeoroid populations, which may depend upon the transmitting–receiving setup and the geometry. Hence, an inexpensive, low-power, portable, passive and automated meteor detection system is demonstrated, which can be highly useful for understanding the meteor phenomena and estimation of atmospheric parameters. [ABSTRACT FROM AUTHOR]

A large time-bandwidth product bistatic radar offers several advantages in high-resolution target detection and motion parameter estimation, but the scale factor and inner-pulse Doppler will also be introduced in the radar echoes when detecting high-speed targets. Under this condition, the conventional matched filter will cause a certain energy loss and a nonnegligible shift of the range center, which is called mismatch effect. Considering the special geometries of the bistatic radar, we first establish the precise echo signal (PES) model to describe the radar echoes for high-speed targets in a space–air-based bistatic radar system. By analyzing the mismatch effect within the pulse and the migration between pulses, we have obtained the mathematic relationship between the scale factor, inner-pulse Doppler shift, range, equivalent velocity, and acceleration. Following that, we perceive that the parameters of the matched filter are related to the target motion parameters, i.e., a priori unknown, which inspires us to propose an iterative coherent integration method to achieve the intrapulse and interpulse integration. A PES model matched filter with initial parameters is defined and a roughly motion parameter estimation is acquired by the PES model-based Keystone transform (PES-KT) and inner-pulse chirp Fourier transform (CFT). The estimated parameters are used for matched filter construction and coherent integration. This process is performed over multiple iterations to provide an accurate motion parameter result. In the end, a target detection experiment is given to show the effectiveness of the proposed method using space–air-based bistatic radar. [ABSTRACT FROM AUTHOR]

Small target detection in high-resolution sea clutter is one of the important problems in radar target detection. We propose a new detector that adopts the visibility graph (VG) algorithm and the graph convolution neural network (GCN). This detector extracts the graph feature of radar echo networks for target detection after converting the phase information of radar echo into complex networks. Different from the existing detector based on graph connectivity, the VG algorithm does not need radar data preprocessing and it can directly convert the radar data into complex networks by judging whether the series data are visible mutually. We extracted the graph feature that can effectively distinguish the background clutter and target echo by GCN instead of calculating the statistic of graphs manually. Experiments on the IPIX radar datasets show that the proposed method achieves superior performance than the existing detector based on graph connectivity, especially in a short observation time. When the observation time is 0.256 s, the average accuracy of #17, #54, and #320 datasets can reach 84.82%, 94.84%, and 90.59%, respectively. [ABSTRACT FROM AUTHOR]

Multiple radar systems represent an attractive option for target tracking because they can significantly enlarge the area coverage and improve both the probability of trajectory detection and the localization accuracy. The presence of multiple extended targets or weak targets is a challenge for multiple radar systems. Moreover, their performance may be severely deteriorated by regions characterized by a high clutter density. In this article, an algorithm for detection and tracking of multiple targets, extended or weak, based on measurements provided by multiple radars in an environment with heavily cluttered regions, is proposed. The proposed method features three stages. In the first stage, past measurements are exploited to build a spatiotemporal clutter map in each radar; a weight is then assigned to each measurement to assess its significance. In the second stage, a track-before-detect algorithm, based on a weighted 3-D Hough transform, is applied to obtain target tracklets. In the third stage, a low-complexity tracklet association method, exploiting a lion reproduction model, is applied to associate tracklets of the same target. Three experiments are presented to illustrate the effectiveness of the proposed approach. The first experiment is based on synthetic data, the second one is based on actual data from a radar network with two homogeneous air surveillance radars, and the third one is based on actual data from a radar network with four different marine surveillance radars. The results reveal that the proposed method can outperform competing approaches. [ABSTRACT FROM AUTHOR]

In this paper, a feature extraction technique based on the electromagnetic (EM) representation of radar signals is presented. In particular, we focus on ground-penetrating radar (GPR) imaging, where we model the backscatter from varying 2-D geometric shapes with arbitrary local coordinate rotations. Due to the electrically small nature of buried targets and the bending of the radar signal at the air–soil interface, we focus on exact methods to model the surface current density induced on scattering surfaces. Overcomplete basis sets are derived from the EM descriptions to represent the scene sparsely. From this proposed modeling framework, we devise a novel methodology to exploit the prediction of scattering behavior to extract features for classification from radar scenes when multiple buried scattering surfaces are present. We see that our method can identify and reconstruct buried scattering geometries in the presence of false targets that are brought about by the nonlinear nature of the exact EM modeling methods. A noniterative algorithm based on the conjugate of Green’s function is developed to solve for the surface current in an unknown domain using multifrequency, multiaperture data. Our modeling and feature extraction algorithms are numerically validated for different target shapes buried in lossy soil profiles. [ABSTRACT FROM AUTHOR]

Standard noise radars, as well as noise-type radars such as quantum two-mode squeezing (QTMS) radar, are characterized by a covariance matrix with a very specific structure. This matrix has four independent parameters: the amplitude of the received signal, the amplitude of the internal signal used for matched filtering, the correlation between the two signals, and the relative phase between them. In this article, we derive estimators for these four parameters using two techniques. The first is based on minimizing the Frobenius norm between the structured covariance matrix and the sample covariance matrix; the second is maximum likelihood (ML) parameter estimation. The two techniques yield the same estimators. We then give probability density functions (pdf’s) for all four estimators. Because some of these pdf’s are quite complicated, we also provide approximate pdf’s. Finally, we apply our results to the problem of target detection and derive expressions for the receiver operating characteristic (ROC) curves of two different noise radar detectors. In summary, our work gives a broad overview of the basic statistical behavior of noise-type radars. [ABSTRACT FROM AUTHOR]

This article proposes a new synthetic aperture radar (SAR), named as panoramic SAR, based on a combination of linear and rotational SARs, by which a large 360° panoramic view of the observed scene can be reconstructed. First, the system geometry and its imaging process based on the back-projection algorithm (BPA) are presented. The combined movement constitutes a 2-D synthetic aperture, and thus higher imaging resolutions can be obtained. The corresponding resolution analysis and the sampling criteria are discussed accordingly. Then, a novel dynamic piecewise compensation (DPC) algorithm, a recursive imaging process, is proposed to reduce the processing complexity significantly. The imaging implementation and the complexity are also studied respectively. Finally, a prototype of panoramic SAR is built based on an frequency-modulated continuous wave (FMCW) radar and a moving platform, and the simulation and experimental results are provided to validate the proposed panoramic SAR principle and the DPC algorithm. [ABSTRACT FROM AUTHOR]

As a classical sparse reconstruction algorithm, iterative soft thresholding (IST) is often used in SAR sparse imaging applications. However, for moving targets, the radar echo contains an unknown phase error, so it is difficult to directly apply the IST algorithm to reconstruct the SAR moving target image. In this article, a parametric IST (P-IST) algorithm is proposed for refocusing of moving targets in synthetic aperture radar (SAR) images. In this algorithm, the echo phase error is modeled as a function of the phase compensation factor, and in the iteration process, a phase compensation factor optimization selection step based on residual energy minimization is added. Compared with the existing algorithms, the proposed P-IST algorithm can obtain the global optimal solution and has better efficiency and robustness. Both simulated data and measured data are used to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System-Reflectometry (GNSS-R) is a promising field with a diverse set of applications. Polarimetric GNSS-R has been shown to be sensitive to different land parameters, such as freeze/thaw, crop growth, or soil moisture. In this article, the polarimetric scenario of a GNSS reflection is defined, and the theoretical basis and the algorithm to retrieve the Stokes parameters of a GNSS-R signal are proposed. The presented algorithms are validated using data collected by the two linear orthogonal polarization channels of the Soil Moisture Active Passive (SMAP) radar antenna working as GNSS-R, known as SMAP-Reflectometry (SMAP- $R$). The Stokes parameter retrieval is presented for the SMAP- $R$ case, and the receiver phase offset and intensity are calibrated. The same calibration procedure proposed by the CYclone GNSS (CYGNSS) mission is applied to the SMAP- $R$ first Stokes parameter, allowing the retrieval of the normalized bistatic radar cross Section (NBRCS) of such measurement. Finally, the SMAP- $R$ data are compared to the CYGNSS NBRCS over the ocean, showing an unbiased root-mean-square error of 3.3 dB and a small bias. [ABSTRACT FROM AUTHOR]

Polarization information can greatly improve the ability of detection, parameter retrieval, and classification for entomological radar. In recent, the instantaneous fully polarimetric measurement technique is used in entomological radar, so as to relax the restriction of invariant orientation during observation compared to the typical vertical-looking radar with linear-polarized rotation configuration. However, the fully polarimetric measurement is based on a multichannels’ radar system, and the imbalance and crosstalk between channels, namely, polarimetric errors, will severely affect the measurement of the polarimetric scattering matrix (PSM). Thus, polarimetric calibration is essential to obtain the accurate polarization information of targets. In contrast to inefficient and manual calibration methods, a data-driven polarimetric calibration method is proposed based on the assumption of reciprocity and bilateral symmetry of insects in this article. The polarimetric errors are elaborately decomposed into two components, which could be estimated by reciprocity and bilateral symmetry, respectively. In addition, because the proposed method is data-driven, the calibration process could be automatically executed to compensate for the temporal-variant polarimetric errors induced by temperature change and, thus, suitable for the long-term operation of entomological radar. Simulations and experiments are carried out to evaluate the performance of the proposed calibration method. Results show that it could achieve high accuracy when the number of insects used as calibrators is large enough. The proposed method has been applied in Ku-band high-resolution fully polarimetric entomological radars for cross-border migratory insect observation in Yunnan province, China. [ABSTRACT FROM AUTHOR]

Compressed sensing (CS)-based synthetic aperture radar (SAR) imaging methods are showing superior potential in imaging performance over classical matched filtering-based methods. However, the CS-based methods require much more computational cost to solve the iterative optimization composed of large-scale matrix operators. To hold the improvement of imaging performance and reduce the computational cost, in this article, we propose a novel SAR imaging method by a deep unfolded network (DUN) of an iterative shrinkage threshold algorithm (ISTA) with the approximated observation of a range-Doppler algorithm (RDA) operator. The proposed method takes the radar echoes as the input to learn the imaging procedure. First, the approximated observation is utilized in an SAR imaging model to reduce the size of the DUN. Moreover, we use ISTA as an example to introduce how to establish DUN with approximated observation, in which the detailed structure to handle the complex-valued radar echoes is also designed. Finally, the autoencoder is utilized to calculate the difference of the echoes rather than the imaging results, so that we can train the proposed network by unsupervised learning. The experiments of point targets, surface targets, and real scenes show that the proposed imaging method is superior in terms of imaging performance and computing efficiency. [ABSTRACT FROM AUTHOR]

We propose a directive antenna for borehole radar using a horizontally polarized wave in a vertical borehole. This antenna is an array of several horizontal loop antennas, arranged vertically. When a plane wave is incident on the antenna, the differences in arrival times at the array elements enable us to estimate the direction of arrival (DOA). We present a simple model representing the arrival time difference and propose an algorithm for DOA estimation. Furthermore, we synthesized the array signal by analyzing the electromagnetic field using the method of moments (MoM) and applied the proposed algorithm to the signal. We found that mutual coupling between antenna elements affects the DOA estimation and that a space of about 3 cm between antenna elements prevents mutual coupling. Using the MoM analysis, we simulated a cross-hole measurement to demonstrate the antenna’s ability. We carried out field experiments in wet soil to examine the antenna’s ability. The proposed loop array antenna in a water-filled borehole received a direct wave from a source in another borehole. After applying the proposed algorithm to the measured data, we found that we could estimate the DOAs to the source with an error of less than 15° in the azimuth angle and 10° in the elevation angle. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting is an important task, which can be used in numerous applications. The key challenge of the task lies in radar echo map prediction. Previous studies leverage the convolutional recurrent neural network (ConvRNN) to address the problem. However, the approaches are built upon mean square losses, and the results tend to have inaccurate appearances, shapes, and positions for predictions. To alleviate this problem, we explore the idea of adversarial regularization and systematically compare four types of generative adversarial networks (GANs), which are the combinations of GAN/Wasserstein GAN (WGAN) and its multiscale version. Extensive experiments on a real-world radar dataset and four typical meteorological examples are conducted. The results validate the effectiveness of adversarial regularization. The developed models show superior performances over the existing prediction approaches in the majority of circumstances. Moreover, we find that the WGAN regularization often delivers better results than the GAN regularization due to its robustness, and the multiscale WGAN, in general, performs the best among all the methods. To reproduce the results, we release the source code at https://github.com/luochuyao/MultiScaleGAN and the test system at http://39.97.217.145:80/. [ABSTRACT FROM AUTHOR]

The total mean square slope (mss) of large-scale (in comparison with the scale of radar wavelengths) ocean waves can describe the roughness of the ocean surface. This important parameter has found many applications and can be determined by the dependence of large-scale mss on azimuthal angles. The Surface Waves Investigation and Monitoring instrument onboard the China France Oceanography Satellite (CFOSAT) can provide a 2-D normalized backscattering radar cross section (NRCS) for 2-D global ocean-surface wave detection. In this article, the 2-D NRCS is used to calculate the total large-scale mss via two methods. The first method is constructed from regression, while the second scheme is derived from a three-solution method. Finally, the total large-scale mss is calculated using these methods, and the map of the distribution of the global total mss is presented for the first time. Our results show that the two sources of total large-scale mss obtained are consistent with each other. CFOSAT can provide a 2-D total large-scale mss map and be used as a new data resource for global applications. [ABSTRACT FROM AUTHOR]

Exploring polarimetric diversity of synthetic aperture radar (SAR) data is directly applicable to conventional monostatic cases. For this, the most used convention is the backscatter alignment. While establishing important advantages for the monostatic case (possibility to have equal values on the cross-polarimetric channels), it has been proven to introduce some difficulties for the bistatic case. This appears in relation to the so-called conjugate similarity operation, when (mathematically) asymmetric scattering matrices occur. In this article, we propose the detailed algorithm which provides a solution to the conjugate similarity operation, in the case of general scattering matrices. The proposed algorithm is based on the real representation (RR) matrix transformation. Furthermore, we investigate the characterization of canonical bistatic scatterers (three elementary targets). Raw bistatic polarimetric signals are obtained using simulations with a computationally electromagnetic (EM) software, capable of complete EM analysis. The eigenvalue classification illustrates the potential of additional information brought using the proposed RR scattering matrix (RRSM). The presence of complex eigenvalues is investigated in relation to the bistatic angle and one nonreciprocity parameter. [ABSTRACT FROM AUTHOR]

Airborne multichannel high-resolution radar (HRR)-ground moving target indication (GMTI) is of great significance to wide-area surveillance, traffic monitoring, and target recognition. The Aerospace Information Research Institute, Chinese Academy of Sciences, produced an advanced airborne digital array radar with high resolution and conducted an experiment with slow and weak cooperative moving targets in 2021. In this article, an overall processing framework for target detection, parameter estimation, and target tracking using this system is introduced. For HRR detection, the echo energy of targets is spread into multiple range units, so-called range-spread targets; thus, using detectors designed for pointlike targets will severely degrade the detection performance, especially for slow and weak targets. To address the adaptive detection of such range-spread targets embedded in Gaussian clutter with an unknown covariance matrix, a novel two-step generalized space–time adaptive processing (GSTAP) algorithm is proposed, which offers an enhanced clutter suppression capability compared with natural competitors. Moreover, a tracking method is implemented in the range-Doppler domain to avoid track loss caused by azimuth relocation error and reject discrete false alarms. Both simulation and experimental results are presented to demonstrate the effectiveness of the proposed method and provide a paradigm for further research. [ABSTRACT FROM AUTHOR]

Spatiotemporal distribution of soil moisture is important for hydrometeorological and agricultural applications. There is growing interest in monitoring soil moisture in relation to soil- and land-based natural flood management (NFM), to understand the soil’s ability, via land-use and management changes, and to delay the arrival of flood peaks in nearby watercourses. This article monitors relative surface soil moisture (rSSM) across the Thames Valley, U.K., using Sentinel-1 data, and the Vienna University of Technology (TU-Wien) Change Detection Algorithm, with a novel exploration of monthly and annual normalization factors and spatial averaging. Two pairs of normalization factors are introduced to remove impacts from varying local incidence angles through direct and multiple regression slopes. The spatiotemporal distribution of rSSM values at various spatial resolutions (1000, 500, 250, and 100 m) is assessed. Comparisons with in situ soil moisture data from the COSMOS-UK network show that, while general temporal trends agree, the difference in effective depth of measurements, coupled with vegetation impacts during the growing season, makes comparison with soil moisture observations difficult. Temporal rSSM trends can be retrieved at spatial resolutions down to 100 m, and the rSSM RMSE was found to decrease as the spatial resolution increases. The vegetation effects upon the rSSM are further explored by comparing the two dominant land cover types: Arable and Horticulture, and Improved Grassland. It was found that, while the rSSM retrieval for these land covers was possible, and the general soil moisture trend is clear, overlying vegetation during the summer artificially increased the rSSM values. [ABSTRACT FROM AUTHOR]

In this article, we propose a novel solution to detect multiple targets using a distributed multiple-input multiple-output (MIMO) radar under the so-called “defocused transmit-defocused receive” operating mode. The proposed method employs a grid-based data matching algorithm, aiming to associate the target responses to potential target locations, solving the resulting data puzzle that evaluates the various cells under test (CUTs) in the surveillance area resulting from the intertwined range cells across all transmit-receive channels. Sketchily, the approach divides the surveillance area into identically interlocking and analytically expressible grid cells and then selects the grid cells with the best fitting multichannel data to be equivalently regarded as the CUTs. Next, the generalized likelihood ratio test (GLRT) detector is derived to test for target presence in each of the selected grid cells. A separate procedure is introduced to eliminate the spurious “shadow targets,” false alarms occurring in the grid cells without a target while sharing range cells with the targets. The essence of this procedure is to find the source of the observed contributions to the grid cells whose test statistics exceed their thresholds, and simultaneously obtain the positions of the targets. The proposed method is evaluated using both numerical simulations and experimental data recorded by five small radars, demonstrating the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

For ultrahigh resolution synthetic aperture radar (SAR), azimuth spectrum aliasing limits the application of frequency-domain autofocus algorithms. Therefore, time-domain autofocus algorithms are often used for ultrahigh resolution SAR imaging. However, the azimuth deramping operation in current time-domain autofocus algorithms may introduce an additional azimuth-dependent phase. This phase can be regarded as a part of the phase error, which significantly reduces the estimation accuracy of the phase error. To address this issue, this article proposes a new time-domain autofocus algorithm based on azimuth scaling transformation (AST) for ultrahigh resolution SAR. In this algorithm, we first adopt the azimuth scaling operation to avoid the azimuth-dependent phase so that the estimation accuracy of error can be greatly improved. Then, for the azimuth-dependent shifts caused by the azimuth scaling operation, we adopt the alignment processing to remove them in azimuth-time domain. Finally, we can estimate the error accurately from the aligned signal. The simulation and measured data were processed to verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Earth-based repeat-pass synthetic aperture radar (SAR) interferometry (InSAR) is a powerful tool to obtain the high-precision lunar terrain and deformation information, due to the short revisit period as well as the all-weather, long-time, and large-coverage characteristics of radar lunar observation. In this article, the baseline formation mechanism is investigated. Theoretical analyses show that the interferometric baselines mainly come from the lunar libration; however, the lunar libration may also result in a significant change of baselines and further lead to serious geometric decorrelation. To address this problem and ensure the Earth-based repeat-pass InSAR of the Moon, the precise relative motion model between the radar and the lunar target, considering lunar libration, is established, and the subradar point (SRP) wagging phenomenon is analyzed. Then, to evaluate the effects of SRP wagging on InSAR coherence, a geometric decorrelation model based on azimuth angle and incident angle is proposed. Based on that, the spatial–temporal baseline for Earth-based repeat-pass InSAR of the Moon is analyzed based on numerical simulation. Results show that the temporal baseline has obvious periodicity with a cycle of about 27 days. When the temporal baseline is an integral multiple of 27, the spatial baseline usually has the minimum value and the geometric decorrelation effect is relatively slight. Moreover, using the geometric decorrelation criteria, we examine the effects of radar frequency, position, and target location on the quantity of viable interferometric pairs, which has guiding significance for the selection of radar system parameters, radar position, and lunar target area. [ABSTRACT FROM AUTHOR]

Borehole radar is a powerful tool for detecting subsurface fractures. Fracture modeling and numerical simulation are essential means to study fracture detectability. In this article, we first propose a method to construct a single fracture model, which combines the Baecher disk model and random rough surface and includes features, such as roughness, pinch-out, and irregularity. As the precise description of the fracture raises the requirement for accuracy of the simulation algorithm, the 3-D conformal finite-difference time-domain (CFDTD) method is used in this work. Numerical simulations of a sphere and rough fractures show that the CFDTD has higher calculation accuracy than conventional FDTD. Then, we analyzed how the fractures with different roughness affect the electromagnetic wave response. It is found that as the fracture surface becomes rougher, the wave scattered by it becomes stronger, more fracture contour-related information is obtained, and the fracture morphology is recovered better. Combined with accurate fracture modeling and high-precision numerical simulation, the electromagnetic response characteristics of different forms of fractures are obtained, which provides a basis for the accurate detection and interpretation of fractures in the future. [ABSTRACT FROM AUTHOR]

With the development of the JUpiter ICy moons Explorer (JUICE)/Radar for Icy Moons Exploration (RIME) [European Space Agency (ESA)] and Europa Clipper/Radar for Europa Assessment and Sounding: Ocean to Near-surface (REASON) [National Aeronautics and Space Administration (NASA)] instruments, designed to study the subsurface of the Galilean moons, interest has been growing to study the performances of sounding radar orbiting these bodies. In the presence of strong Jupiter’s radio emissions, probing in a passive mode using these emissions as the emitter is considered. However, radar performances in this bistatic mode are dependent on the entire geometry of observation: the position of the source of emission, the spacecraft trajectory, and on the region probed. The 3-D Simulations are necessary to estimate the performances of these measurements. We analyze the influence of the geometry of observation by approximating Jovian radio bursts as radio impulses, simulating the signal scattered by a point target in a realistic 3-D geometry and computing the resolution. This allows a preliminary identification of scenarios of observation best suited for this radar mode. The influence of the correct localization of Jupiter’s emissions on the synthetic aperture radar (SAR) image is investigated as well, and interest in recovering the true position of Jupiter’s source is highlighted. [ABSTRACT FROM AUTHOR]

Global navigation satellite system (GNSS)-based passive bistatic radar (PBR) has many advantages benefiting from the bistatic configuration and the excellent properties of the GNSS signals. In this article, the possibility of moving target indication (MTI) using a GNSS-based PBR is analyzed and illustrated by experiments. The power budget is first evaluated to investigate the capabilities and limitations of the GNSS-based PBR. Due to the low power density of GNSS signals, long-time integration is required to achieve long-range surveillance. In our previous work, a Radon Fourier transform (RFT)-based long-time integration algorithm has been proposed for GNSS-based PBR. However, the RFT-based methods are computationally inefficient. In this article, a novel MTI algorithm based on high frame rate image sequence (HFRIS) is introduced to the GNSS-based PBR. Benefiting from the avoiding of the iterative multidimensional parameter searching, it has a higher computational efficiency than the RFT-based methods. At the same time, the possibility of multistatic positioning for moving targets using the GNSS-based PBR is analyzed. It is shown that the absolute position of the target in three dimensions could be determined if at least three satellites are utilized to measure the range delay of the target return. To confirm our analysis, experiments are conducted which detects the airplanes using the GPS signals as illuminators of opportunity. The collected data are processed by both the RFT-based method and the HFRIS-based method. Both methods have successfully detected the airplane target at a distance of about 5 km and the HFRIS-based method shows a far better performance in terms of processing efficiency. The multistatic positioning experiment is also conducted and the estimated position of the target is well consistent with the values acquired by the flight record, which shows the potential of the GNSS-based PBR on multistatic operations. [ABSTRACT FROM AUTHOR]

The micro-Doppler (m-D) modulations to radar backscattering introduced by the flexible body articulations and complicated movement patterns of human movers can provide valuable information for activity classification and help to identify the interested targets. In particular, the m-D signal of limbs, as a highly distinctive feature of human activities, can be used as an effective clue to discriminate between the armed and unarmed persons, as well as the humans against other small animals. In this article, a novel theoretical method is proposed to extract the target m-D trajectories through an integrated application of modified Viterbi algorithm and Hough transform. Through this method, multiple components corresponding to various target scattering parts and their respective m-D trajectories can be accurately extracted and estimated, even in the overlapping regions of different scattering parts in the time–frequency (TF) distribution. The employed search method enhances upon the traditional ones and improves the efficiency of finding the optimal paths considerably. Finally, a series of experiments is conducted to illustrate the validity and performance of the proposed techniques. Compared to short-time Fourier transform (STFT) peak detection and traditional Viterbi algorithm, the average error of m-D frequency estimated by the proposed algorithm is reduced by 82.1% and 71.8%, respectively. Besides, the processing time is reduced by 36.4% compared to the traditional Viterbi algorithm. [ABSTRACT FROM AUTHOR]